Distributed quantum computing (DQC) connects multiple quantum processing units (QPUs) via quantum interconnects to overcome the qubit-count and connectivity limitations of individual devices. A critical challenge in this paradigm is routing entanglement resources efficiently across the quantum interconnect network so that inter-QPU quantum gates can be executed with high fidelity and minimal resource waste. We propose the Entanglement-Aware Adaptive Routing (EAAR) framework, which jointly optimizes path selection, entanglement purification scheduling, and congestion-aware load balancing for DQC workloads. EAAR models the quantum network as a fidelity-weighted directed graph and transforms the maximum-fidelity path problem into an additive shortest-path problem. An adaptive purification scheduler targets bottleneck links when end-to-end fidelity falls below application-defined thresholds, and a dynamic congestion penalty steers traffic away from over-utilized entanglement buffers. We evaluate EAAR in simulation across grid, ring, random-regular, and small-world topologies ranging from 4 to 24 QPU nodes. Results demonstrate that EAAR improves average remote-gate fidelity by up to 10.3% and reduces entanglement consumption by up to 44.8% compared to greedy baselines, while maintaining competitive latency with shortest-path routing. These findings confirm that holistic, entanglement-aware routing is essential for practical, high-fidelity distributed quantum computation.
Distributed quantum computing (DQC) connects multiple quantum processing units (QPUs) via quantum interconnects to overcome the qubit-count and connectivity limitations of individual devices. A critical challenge in this paradigm is routing entanglement resources efficiently across the quantum interconnect network so that inter-QPU quantum gates can be executed with high fidelity and minimal resource waste. We propose the Entanglement-Aware Adaptive Routing (EAAR) framework, which jointly optimizes path selection, entanglement purification scheduling, and congestion-aware load balancing for DQC workloads. EAAR models the quantum network as a fidelity-weighted directed graph and transforms the maximum-fidelity path problem into an additive shortest-path problem. An adaptive purification scheduler targets bottleneck links when end-to-end fidelity falls below application-defined thresholds, and a dynamic congestion penalty steers traffic away from over-utilized entanglement buffers. We evaluate EAAR in simulation across grid, ring, random-regular, and small-world topologies ranging from 4 to 24 QPU nodes. Results demonstrate that EAAR improves average remote-gate fidelity by up to 10.3% and reduces entanglement consumption by up to 44.8% compared to greedy baselines, while maintaining competitive latency with shortest-path routing. These findings confirm that holistic, entanglement-aware routing is essential for practical, high-fidelity distributed quantum computation.
The quantum internet enables secure communication, distributed quantum computing, and remote sensing by distributing entanglement among distant nodes [1], [2]. However, establishing end-to-end entanglement across regional networks remains challenging because quantum links experience photon loss, entanglement generation is probabilistic, quantum memories have limited coherence times, and repeater resources are constrained [2], [3]. Conventional shortest-path routing does not jointly account for these quantum-specific limitations. This paper proposes a Fidelity- and Coherence-Aware Dynamic Entanglement Routing (FCADR) framework for regional quantum internet networks. The network is modelled as a graph containing end nodes, quantum repeaters, quantum memories, and lossy entanglement links. FCADR selects routes by jointly considering estimated end-to-end fidelity, entanglement-generation and swapping success probabilities, available quantum-memory capacity, and expected distribution delay. A discrete-event simulation framework is designed to compare FCADR with shortest-path routing, link-success-based routing, and fidelity-aware routing under varying channel loss, coherence time, traffic load, and node or link failures. Performance is evaluated using end-to-end fidelity, request-success rate, distribution delay, memory utilization, and network resilience. The framework aims to improve routing decisions by incorporating quantum-state quality and resource availability into dynamic path selection.
In recent years, advances in quantum computing have been driven by substantial improvements in both the number and quality of qubits. As the field progresses, there is growing interest in interconnecting quantum systems to enable scalable computation through Distributed Quantum Computing (DQC) architectures. Consider a distributed quantum application executed across multiple Quantum Processing Units (QPUs) within a quantum data center, where remote gates require establishing entanglement between different QPUs. The creation of such end-to-end entanglement can lead to network congestion and resource contention. To address these challenges, we propose a resource management framework that maximizes fidelity-guaranteed throughput while satisfying dependency constraints. We first formulate the problem as a Mixed-Integer Linear Programming (MILP) model to provide a performance benchmark. Building on this, we develop efficient approximate scheduling algorithms that achieve performance comparable to the optimization solver. Although a trade-off exists between execution time and network throughput, simulation results demonstrate that one of the proposed strategies, Weighted Group Least Resource First (WGLRF), closely approximates the solver’s performance across most scenarios. These findings suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.
Abstract Future quantum networks will have to support different applications on shared infrastructure, like fidelity-critical quantum key distribution (QKD) and latency-sensitive distributed quantum computing (DQC). The existing approaches for entanglement routing either optimise for a single application, or rely on sequential resource allocation, which results in inefficient resource utilisation, unfairness and degraded performance when multiple applications compete for shared network resources. In this paper, we propose AMARQ, a multi-objective deep Q-network based routing framework to jointly serve heterogeneous QKD and DQC traffic on shared quantum infrastructure. The agent makes adaptive and application-aware routing decisions to dynamically balance the rigid fidelity and latency requirements of QKD and the high-throughput and elastic rate requirements of DQC. The reward function combines a successful-routing throughput term with a per-edge fidelity penalty and an end-of-episode failure penalty; per-flow latency is enforced as a QoS constraint, and inter-application fairness is enforced by a dedicated admission-control loop that reserves a dynamic QKD qubit pool. We assess the performance of AMARQ on grid topologies with 25 to 625 nodes. AMARQ attains a request success ratio above 85%, which is up to 32 percentage points higher than ESDI and 39 percentage points higher than Cicconetti, while keeping the end-to-end fidelity above 0.80 and the inference latency below 90 ms for all configurations.
Distributed Quantum Computing (DQC) enables scalable quantum execution by interconnecting multiple quantum processing units (QPUs) through quantum networks. In DQC, end-to-end performance is jointly affected by circuit partitioning, entanglement routing, scheduling, and heterogeneous hardware characteristics. However, existing studies often optimize these components independently, providing limited understanding of their cross-layer interactions. In this paper, we present a cross-layer joint-optimization study for DQC using the previously developed SimDisQ-Net simulator. Through simulations, we find that circuit orchestration is one of the dominant factors affecting distributed execution quality, while network-layer mechanisms provide secondary but still meaningful improvements. We further demonstrate that traditional communication metrics, such as hop count or remote-gate count alone, are insufficient predictors of execution quality due to the strong interaction among path fidelity, hardware characteristics, and circuit structure. Motivated by these findings, we propose a topology-aware fidelity proxy (TAFP) evaluation approach that approximates distributed execution fidelity, enabling efficient evaluation of candidate circuit optimizations without time-consuming simulation. Our results highlight the importance of integrated circuit-network orchestration for scalable DQC.
Quantum network can provide end-to-end entanglements through quantum operations, supporting various quantum applications such as quantum key distribution and distributed quantum computing. However, many previous proposals for quantum networks assumed the division of quantum network into time slots and batch processing of network communication tasks, performing poorly in dynamic network scenarios and not fully utilizing network resources. This work focuses on the routing problem in entanglement quantum networks, aiming to provide more efficient end-to-end entanglement establishment for upper-layer applications. We propose an on-demand routing algorithm that dynamically adjusts the optimal path based on the current network conditions. Specifically, we consider the benefits of path width for quantum networks and propose an algorithm for finding the shortest wide path. Using this algorithm, each node establishes its own routing table. Additionally, to dynamically avoid network hotspots, we introduce the concept of traffic to reflect the busyness of nodes. Finally, using a dynamic cost calculation method as the routing metric, we select the optimal path for the current request. To evaluate the performance of the proposed quantum routing algorithm, we conducted simulation experiments on SimQN quantum simulation platform. The results show that our approach improves network throughput by 14 percent compared to existing solutions.